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首页> 外文期刊>Applied Soft Computing >On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems
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On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems

机译：具有自适应参数，交叉算子和均方根（RMS）变量的粒子群优化算法的改进性能，用于计算一类混合系统的最优控制

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摘要

This paper deals with the concept of including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants into particle swarm optimization (PSO) algorithm to make the convergence faster. Two different PSO algorithms are considered in this paper: the first one is the conventional PSO (cPSO) and the second is the global-local best values based PSO (GLbest-PSO). The GLbest-PSO includes global-local best inertia weight (GLbestIW) with global-local best acceleration coefficient (GLbestAC), whereas the cPSO has a time varying inertia weight (TVIW) and either time varying acceleration coefficient (TVAC) or fixed AC (FAC). The effectiveness of the cross-over operator with both PSO algorithms is tested through a constrained optimal control problem of a class of hybrid systems. The experimental results illustrate the advantage of PSO with cross-over operator, which sharpens the convergence and tunes to the best solution. In order to compare and verify the validity and effectiveness of the new approaches for PSO, several statistical analyses are carried out. The results clearly demonstrate that the GLbest-PSO with the cross-over operator is a very promising optimization technique. Similar conclusions can be made for the GLbest-PSO with RMS variants also.
机译：本文讨论了将流行的遗传算法算子，交叉和均方根（RMS）变体纳入粒子群优化（PSO）算法的概念，以加快收敛速度​​。本文考虑了两种不同的PSO算法：第一种是常规PSO（cPSO），第二种是基于全局局部最优值的PSO（GLbest-PSO）。 GLbest-PSO包括具有全局局部最佳加速度系数（GLbestAC）的全局局部最佳惯性权重（GLbestIW），而cPSO具有随时间变化的惯性权重（TVIW）和随时间变化的加速度系数（TVAC）或固定AC（ FAC）。通过一类混合系统的约束最优控制问题，测试了具有两种PSO算法的交叉算子的有效性。实验结果说明了具有交叉算子的PSO的优势，它可以提高收敛性并调整为最佳解决方案。为了比较和验证新的PSO方法的有效性和有效性，进行了一些统计分析。结果清楚地表明，带有交叉算子的GLbest-PSO是非常有前途的优化技术。对于带有RMS变体的GLbest-PSO也可以得出类似的结论。

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